ADVANCEMENT IN CIRCUIT TECHNOLOGIES FOR ENERGY HARVESTING SYSTEMS

The photovoltaic cells’ output characteristics and maximum power point tracking (MPPT) technology are studied to design a two-stage cascaded photovoltaic off-grid inverter, including the front-end Boost converter and the rear-end three-phase inverter. The state-space averaging method is adopted in front-end Boost converter modeling, and space vector pulse width modulation (SVPWM) technology is adopted in the rear-end three-phase inverter. The simulation drive model is obtained through research principles and algorithm realization. Based on the theoretical analysis and simulation, front-end Boost converter’s and the back-end inverter’s hardware are designed. The main circuit includes the front-end Direct Current (DC)-DC topology and the back-end DC-AC topology. The TMS320F28027 digital signal processor is the core of the front-end DC-DC topology’s control system, and LabView is the core of the back-end DC-AC topology’s control system. The front-end Boost converter’s analog output voltage is 32 V, stable at the maximum operating point in the test. The maximum power point can be effectively tracked in the proposed inverter. The rear-end three-phase inverter’s total harmonic distortion (THD) of phase voltage is less than 3%. Less harmonic wave meets the circuit harmonic distortion rate’s design requirements.

The Maximum Power Point Tracking technology (MPPT) is a basic technology which can improve the efficiency of photovoltaic power generation system. Improving the tracking accuracy and speed and reducing the probability of mis-tracking is the basic research direction in MPPT. In view of the insufficiency in the tracking speed and accuracy of the traditional hysteresis comparison method, this paper proposes an improved hysteresis comparison algorithm, which improve the tracking speed and steady precision of MPPT, additionally ,which can avoid the mistracking problem with a faster dynamic tracking speed in the case of the environment variation . The results of experiment and Matlab/Simulink simulation proved that the improved method has a faster speed and more accuracy in tracking the maximum power point of PV array, and the improved method can stably work in a dynamic environment.

Los controladores de carga son implementados en varios sistemas electrónicos con el objetivo de proteger y controlar la carga y descarga de una batería; en el caso de los controladores utilizados en sistemas fotovoltaicos autónomos se implementan dos tipos de tecnologías, Pulse Width Modulation (PWM) y Maximum Power Point Tracking (MPPT). En este artículo se compararon dos controladores de carga con diseños originales en sistemas fotovoltaicos con las mismas especificaciones técnicas para determinar el comportamiento de cada uno bajo condiciones ambientales similares. La implementación de ambos controladores de carga se basó en software y hardware con diseños originales, utilizando tecnología PWM y MPPT. Ambos sistemas están compuestos por el controlador de carga, un panel solar de 30 W y una batería de 12 V a 18 Ah; se realizaron las pruebas experimentales de ambos controladores midiendo voltaje y corriente en el panel y en la batería en procesos de carga y descarga, observando que el controlador MPPT tiene una eficiencia promedio mayor que el controlador PWM debido a que el tipo de tecnología implementada influye directamente en la eficiencia, incluso ante valores menos favorables de radiación solar y temperatura ambiente. El controlador PWM es una opción de eficiencia aceptable y además de bajo costo respecto al controlador MPPT. En la implementación de ambos controladores se calcularon tiempos de autonomía similares.

Under partial shading conditions, the system operating sequence of photovoltaic cells does not fall on a single characteristic curve, and the traditional maximum power point tracking (MPPT) control algorithm is prone to causing the system output power to oscillate around the maximum power point. In the case of multiple peaks, the traditional MPPT algorithm is prone to being trapped in a local optimum solution. This paper proposes an MPPT control algorithm based on the WOA-RF prediction algorithm combined with a variable step disturbance observation method. By establishing a photovoltaic system simulation model, the improved algorithm is verified through simulation. The improved MPPT control algorithm can adaptively adjust the step size under different conditions to ensure that the system can quickly and accurately track the maximum power point. At the same time, by optimizing the algorithm logic and parameter settings, it can effectively avoid problems such as local optimum solutions and instability in the system, and improve the system convergence speed and tracking accuracy.

In the case of partial shading conditions, there will be more than one maximum power point (MPP) in photovoltaic (PV) array. The traditional maximum power point tracking (MPPT) methods are easy to get in the local maximum power point (LMPP) and fail. Based on the standard differential evolution (DE) algorithm, the mutation strategy, scaling factor F, and cross factor CR of the algorithm are optimized. Also, the population position variance δ 2 is used to prevent falling into LMPP. Finally, the conditions for algorithm termination and restart are set. It is verified by simulation that the method has fast convergence speed, high accuracy, and can adapt well to changes in the external environment. The improved DE algorithm has a great advantage in MPPT.

Solar Charge Controller (SCC) with Maximum Power Point Tracking (MPPT) is needed to extract maximum energy from photovoltaic. However, a SCC device with MPPT technology feature is expensive on the market due to the requirements for a high-power system. On the other hand, in lower power applications such as IoT sensors, solar street lights, and wireless communication nodes, these types of controllers can be produced at a lower cost. In this study, the design of a low-cost SCC was conducted using the MPPT technology for low-power solar applications. The SCC is designed based on the Arduino microcontroller, which has the role of controlling the circuit and producing PWM signals to regulate the DC-DC converter. Several tests were conducted to validate the efficiency of the MPPT algorithm. The SCC device succeeded in increasing efficiency up to 52% on the low irradiance level.

The performance of perovskite solar cells (PSCs) has been improved throughout the years. These photovoltaic (PV) cells can be used to power Internet of Things (IoT) devices for indoor applications. A perovskite PV energy harvesting system with a stand-by battery that continuously powers an IoT device is developed in this work. The battery is required to complement the PSCs when the latter have difficulties in power delivery during low or no irradiance. The performance of the energy harvesting circuit as well as the battery charge and discharge scenarios are investigated. Voltage matching between the PSC and the battery is achieved by a boost converter. The PSC energy harvesting system uses fractional open-circuit voltage (FOCV) based maximum power point tracking (MPPT), which utilizes a Sample and Hold (S&H) circuit. The FOCV technique is based on a comparison of the perovskite PV open circuit voltages and the maximum power points. For each irradiance level, the maximum power point is unique, and this work uses a light-dependent resistor (LDR) to adjust the scaling constant in MPPT. Case studies include various scenarios under 1000 lux fluorescent light and 1 sun irradiance as well as a consideration of different battery states.

The increasing demand for energy and the environmental impact from the excessive use of fossil fuels has increased the interest in the renewable research. Solar energy is increasingly becoming an area of interest to researchers due to its abundance source and accessibility. This can be achieved by utilizing photovoltaic (PV) panels. Nevertheless, due to PV nonlinear characteristics, maximum power point tracking (MPPT) algorithm are applied. Many MPPT techniques and improvement have been reported in the literature. In this paper, the impact of temperature and irradiation on the performance of proportionality constants in both fractional open circuit voltage (FOCV) MPPT and fractional short circuit current (FSCC) MPPT techniques are explored. Normally in these algorithms the irradiation effect is solely considered. However, the obtained results show that the value of these constants can be re-tuned for better energy harvesting when both the temperature and the irradiation change.

This paper presents a half breed sustainable age framework at an isolated region empowered from sun powered and wind energy sources. DFIG (Doubly Induction Generator) outfitted with MPPT (Maximum Power Point Tracking Technology) is used to remove wind energy. A glasslike sun situated photovoltaic (PV) system is used to change over sun oriented power which is overseen at the standard DC transport of DFIG. The sun based power is supported through DC help converter which is moreover outfitted with MPPT computation to isolate most outrageous sun situated energy. A battery bank is related at the ordinary DC transport of the DFIG which goes about as support storing for exchange of energy. The system is planned for complete customized job taking idea of the multitude of practical circumstances. The structure is similarly given a game plan of external power support for battery charging. The voltage and repeat are controlled through a modified underhanded vector control of the load side converter which is combined with hang ascribes. It changes the repeat set point considering the energy level of the battery. The structure is exhibited in Sim-Power System apparatus reserve of MATLAB and its execution is reproduced under moving circumstances for example unviability of wind or sun based energy, inconsistent and nonlinear weights has been shown. Keywords Renewable energy sources, solar power generation, wind power, Hybrid system, MPPT Controller, micro grid.

The great development witnessed by investments in renewable energy has made it the focus of researchers’ attention in order to increase its efficiency. This is due to the increase in demand for electrical energy due to rapid technological growth, increase in population numbers, and high fuel prices that are used in the production of traditional electrical energy, but it suffers from a problem that is greatly affected by two factors, namely, the change in the intensity of solar irradiation and temperature, which makes its electrical characteristics non-linear, which causes a decrease in its efficiency. To address the efficiency problem, the researchers developed several techniques for tracking the MPP point and extracting the maximum energy from the solar panels under various measurement conditions. Maximum power point tracking technology (MPPT) technology is the most widely used technology in solar energy systems. In this article, MPPT technology is simulated using MATLAB/Simulink for the purposes of extracting maximum power and managing the duty cycle of a DC-DC buck converter. The performance of the photovoltaic system under various irradiance fluctuations and settings of constant temperature could well be determined using simulation results. Under standard and varied test settings, allowing the inverter to convert over 99% of the electricity provided by the solar panels.

Solar and wind are the most prevalent resources due to their accessibility and the simplicity of their conversion into power. Each renewable resource necessitates a DC/DC boost converter with MPPT management for power generation. The maximum power point tracking (MPPT) technology is utilized to achieve optimal efficiency in photovoltaic (PV) and wind energy systems. Perturbation and Observation-MPPT approach is employed in photovoltaic systems where direct current voltage serves as the perturbation variable. In wind energy systems, a perturbation variable in the form of direct current is utilized in the modified perturb and observe maximum power point tracking algorithm. The modified perturb and observe technique exhibits stability and rapid tracking in response to abrupt changes in wind speed. The greatest Power Point Tracking (MPPT) technique employed with a boost converter optimally takes the greatest power from the available source. The simulation of both renewable energy sources is conducted independently in PSIM 9.0 utilizing various MPPT approaches.

Energy storage remains a strategic challenge in energy control. The world is turning to decentralized generators, including fuel cell systems, which use hydrogen as fuel. This gives a special interest to the system of storage of the hydrogen produced mainly by the solar energy. The electrolyzer becomes the most prominent device, but its disadvantage is that it requires a lot of electrical energy. This present paper deals with controlling the power emanating from the photovoltaic system and consumed by the electrolyzer, through the use of neural networks to optimize the power generated by the photovoltaic system. In this work, we will compare between two types of converters Cuk and SEPIC because they are the most widely used and are two of the developed family of the converter. This paper presents under MATLAB/Simulink the use of Cuk converter with maximum power point tracking (MPPT) technology, to increase its efficiency by an algorithm Perturb and Observe (P&O) and incremental conductance method, then we will apply artificial neural network (ANN) to avoid the disadvantages of MPPT Classical. The MPPT developed with the artificial neural networks presents a better behavior than the classic system Perturb & Observe.

Fuzzy-based maximum power point tracking (MPPT) control system for photovoltaic power generation system

A bidirectional high-voltage (HV) dual-input (DI) buck converter and a fully integrated maximum power point (MPP) tracker for a triboelectric (TE) energy-harvesting (EH) system are proposed. The proposed MPP tracker carries out the fractional open-circuit voltage (FOCV) method without any external resistor for voltage down conversion. The proposed buck converter regulates two high DC voltages from a triboelectric nanogenerator (TENG) up to 70 V with a single shared inductor. By reducing the capacitance at the switching node, the power conversion efficiency is improved, and the maximum end-to-end efficiency is 70.72%, which is 21.15% higher than prior work.

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